Understanding genetic, neurophysiological, and experiential influences on the development of executive functioning: the need for developmental models
Bibliographic record
Abstract
Flexibility is a cornerstone of adaptive behavior and is made possible by a family of processes referred to collectively as executive functions. Executive functions vary in efficacy from individual to individual and also across developmental time. Infants and young children, for example, have difficulty flexibly adapting their behavior, and often repeat actions that are no longer appropriate. And although older children do not typically make such striking errors, they have more difficulty exercising control than adolescents and adults. Such developmental variability parallels (at least in some respects) inter-individual variability in executive functions. Individuals who suffer damage or dysfunction in regions of the prefrontal cortex, for example, often experience difficulty in flexibly adapting their behavior to changes in context. As well, genetic differences between individuals are strongly associated with differences in executive control. Parallels between developmental and inter-individual variability suggest hypotheses about possible mechanisms underlying the development of executive functions but carry risks when interpreted improperly. Overcoming these pitfalls will require mechanistic characterizations of executive functioning that are more deeply rooted in developmental principles. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".